{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pinecone-io/examples/blob/master/learn/search/faiss-ebook/locality-sensitive-hashing-random-projection/faiss_implementation.ipynb) [![Open nbviewer](https://raw.githubusercontent.com/pinecone-io/examples/master/assets/nbviewer-shield.svg)](https://nbviewer.org/github/pinecone-io/examples/blob/master/learn/search/faiss-ebook/locality-sensitive-hashing-random-projection/faiss_implementation.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "# now define a function to read the fvecs file format of Sift1M dataset\n",
    "def read_fvecs(fp):\n",
    "    a = np.fromfile(fp, dtype='int32')\n",
    "    d = a[0]\n",
    "    return a.reshape(-1, d + 1)[:, 1:].copy().view('float32')\n",
    "\n",
    "# 1M samples\n",
    "wb = read_fvecs('../../../../data/sift/sift_base.fvecs')\n",
    "# queries\n",
    "xq = read_fvecs('../../../../data/sift/sift_query.fvecs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000000, 128)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import faiss\n",
    "\n",
    "d = wb.shape[1]\n",
    "nbits = 4\n",
    "\n",
    "# initialize the index using our vectors dimensionality (128) and nbits\n",
    "index = faiss.IndexLSH(d, nbits)\n",
    "# then add the data\n",
    "index.add(wb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[26, 47, 43,  2,  6, 70, 73, 74, 25,  0]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xq0 = xq[0].reshape(1, d)\n",
    "# we use the search method to find the k nearest vectors\n",
    "D, I = index.search(xq0, k=10)\n",
    "# the indexes of these vectors are returned to I\n",
    "I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[31., 15., 47., ...,  0.,  1., 19.],\n",
       "       [ 0.,  0.,  0., ...,  0., 30., 72.],\n",
       "       [22., 15., 43., ...,  0.,  0., 12.],\n",
       "       ...,\n",
       "       [ 6., 15.,  9., ..., 12., 17., 21.],\n",
       "       [33., 22.,  8., ...,  0., 11., 69.],\n",
       "       [ 0., 16., 35., ..., 25., 23.,  1.]], dtype=float32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can retrieve the original vectors from wb using these indexes\n",
    "wb[I[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.7344476 ],\n",
       "       [0.6316513 ],\n",
       "       [0.6995599 ],\n",
       "       [0.20448919],\n",
       "       [0.3054034 ],\n",
       "       [0.25432232],\n",
       "       [0.30497947],\n",
       "       [0.341374  ],\n",
       "       [0.6914262 ],\n",
       "       [0.26704744]], dtype=float32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and calculate the cosine similarity between each of these vectors and xq[0]\n",
    "cosine_similarity(wb[I[0]], [xq[0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why is the index performing so poorly? Well, we already know why. All of these vectors are stored in the LSH index under the same 4-digit, low resolution hash. We can confirm this by checking the other variable returned from our search, `D` - the *distances* calculated by our LSH index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A perfect *zero* for every single item. We know that the *Hamming distance* can only be **0** for perfect matches. So, there is only one hash bucket that will score **0**. This means that we can check how many of our vectors have been stored within this single LSH bucket by finding the point that the Hamming distance reaches 1? Let's search again, this time increasing `k` until we return a `D` array that contains more than just *zeros*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "172100\n"
     ]
    }
   ],
   "source": [
    "k = 100\n",
    "xq0 = xq[0].reshape(1, d)\n",
    "\n",
    "while True:\n",
    "    D, I = index.search(xq0, k=k)\n",
    "    if D.any() != 0:\n",
    "        print(k)\n",
    "        break\n",
    "    k += 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 1., 1., 1.]], dtype=float32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D  # we will see both 0s, and 1s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1.]], dtype=float32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D[:, 172_039:172_041]  # we see the hash code switch at position 172_039"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What `nbits` value gives us a better chance for a more sparse distribution of our vectors?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nbits == 2\n",
      "1000000 / 4 = 250000.0\n",
      "nbits == 4\n",
      "1000000 / 16 = 62500.0\n",
      "nbits == 8\n",
      "1000000 / 256 = 3906.25\n",
      "nbits == 16\n",
      "1000000 / 65536 = 15.2587890625\n",
      "nbits == 24\n",
      "1000000 / 16777216 = 0.059604644775390625\n",
      "nbits == 32\n",
      "1000000 / 4294967296 = 0.00023283064365386963\n"
     ]
    }
   ],
   "source": [
    "for nbits in [2, 4, 8, 16, 24, 32]:\n",
    "    buckets = 1 << nbits\n",
    "    print(f\"nbits == {nbits}\")\n",
    "    print(f\"{wb.shape[0]} / {buckets} = {wb.shape[0]/buckets}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5424448\n",
      "0.560827\n",
      "0.6372647\n",
      "0.6676912\n",
      "0.7162514\n",
      "0.7048228\n"
     ]
    }
   ],
   "source": [
    "xq0 = xq[0].reshape(1, d)\n",
    "k = 100\n",
    "\n",
    "for nbits in [2, 4, 8, 16, 24, 32]:\n",
    "    index = faiss.IndexLSH(d, nbits)\n",
    "    index.add(wb)\n",
    "    D, I = index.search(xq0, k=k)\n",
    "    cos = cosine_similarity(wb[I[0]], xq0)\n",
    "    print(np.mean(cos))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...................."
     ]
    }
   ],
   "source": [
    "xq0 = xq[0].reshape(1, d)\n",
    "k = 100\n",
    "\n",
    "results = pd.DataFrame({\n",
    "    'nbits': [],\n",
    "    'cosine_sim': []\n",
    "})\n",
    "\n",
    "for epoch in range(20):\n",
    "    print('.', end='')\n",
    "    for nbits in [2, 4, 8, 16, 24, 32, 64, 128]:\n",
    "        index = faiss.IndexLSH(d, nbits)\n",
    "        index.add(wb)\n",
    "        D, I = index.search(xq0, k=k)\n",
    "        cos = cosine_similarity(wb[I[0]], xq0)\n",
    "        df = pd.DataFrame({\n",
    "            'cosine_sim': cos.reshape(cos.shape[0])\n",
    "        })\n",
    "        df['nbits'] = nbits\n",
    "        results = results.append(df, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='nbits', ylabel='cosine_sim'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.lineplot(data=results, x='nbits', y='cosine_sim')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assessing Issues\n",
    "\n",
    "We can also extract the vectors from our index, first let's initialize a new index with `nbits == 4`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbits = 4\n",
    "index = faiss.IndexLSH(d, nbits)\n",
    "index.add(wb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And extract the actual binary vectors stored inside our LSH index like so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5, 12,  5, ..., 15, 13, 12], dtype=uint8)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract index binary codes (represented as int)\n",
    "arr = faiss.vector_to_array(index.codes)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000000,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we see that there are 1M of these values, 1 for each vector\n",
    "arr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can convert these into our binary vector format like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 1, 0],\n",
       "       [0, 0, 1, 1],\n",
       "       [1, 0, 1, 0],\n",
       "       ...,\n",
       "       [1, 1, 1, 1],\n",
       "       [1, 0, 1, 1],\n",
       "       [0, 0, 1, 1]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin_vecs = (((arr[:, None] & (1 << np.arange(nbits)))) > 0).astype(int)\n",
    "bin_vecs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And convert them into strings for bucketing too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_arr = np.apply_along_axis(lambda x: ''.join(x), 1, bin_vecs.astype(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0000',\n",
       " '0001',\n",
       " '0010',\n",
       " '0011',\n",
       " '0100',\n",
       " '0101',\n",
       " '0110',\n",
       " '0111',\n",
       " '1000',\n",
       " '1001',\n",
       " '1010',\n",
       " '1011',\n",
       " '1100',\n",
       " '1101',\n",
       " '1110',\n",
       " '1111']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_bins = ['0'* (6-len(bin(x))) + bin(x)[2:] for x in range(16)]\n",
    "order_bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the distribution of vectors across our buckets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/anaconda3/envs/ml/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15]),\n",
       " [Text(0, 0, '0000'),\n",
       "  Text(1, 0, '0001'),\n",
       "  Text(2, 0, '0010'),\n",
       "  Text(3, 0, '0011'),\n",
       "  Text(4, 0, '0100'),\n",
       "  Text(5, 0, '0101'),\n",
       "  Text(6, 0, '0110'),\n",
       "  Text(7, 0, '0111'),\n",
       "  Text(8, 0, '1000'),\n",
       "  Text(9, 0, '1001'),\n",
       "  Text(10, 0, '1010'),\n",
       "  Text(11, 0, '1011'),\n",
       "  Text(12, 0, '1100'),\n",
       "  Text(13, 0, '1101'),\n",
       "  Text(14, 0, '1110'),\n",
       "  Text(15, 0, '1111')])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "sns.countplot(dist_arr, orient='v', order=order_bins)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay we can see that there are definitely far too many items in each bucket - remember that we cannot differentiate vectors that have been placed into the same bucket, so we can tell from this that our `nbits` value *must* be increased.\n",
    "\n",
    "## Visualizing Performance\n",
    "\n",
    "Now, let's take a look at *which* `nbits` values produce the best performance and speed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "....."
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "k = 50\n",
    "\n",
    "# initialize results dataframe\n",
    "results = pd.DataFrame({\n",
    "    'search_time': [],\n",
    "    'recall': [],\n",
    "    'nbits': [],\n",
    "    'nb': []\n",
    "})\n",
    "\n",
    "for epoch in range(5):\n",
    "    print('.', end='')\n",
    "    for nb in [50_000, 100_000, 250_000, 500_000, 750_000, 1_000_000]:\n",
    "        # exhaustive search results\n",
    "        index = faiss.IndexFlatL2(d)\n",
    "        index.add(wb[:nb])\n",
    "        start = datetime.now()\n",
    "        D, I = index.search(xq0, k)\n",
    "        flat_time = (datetime.now() - start).microseconds\n",
    "        target_ids = I[0].tolist()\n",
    "        # LSH search\n",
    "        for nbits in [64, 128, 256, 512, 768, 32]:\n",
    "            index = faiss.IndexLSH(d, nbits)\n",
    "            index.add(wb[:nb])\n",
    "            start = datetime.now()\n",
    "            D, I = index.search(xq0, k)\n",
    "            lsh_time = (datetime.now() - start).microseconds\n",
    "            matches = [x for x in I[0].tolist() if x in target_ids]\n",
    "            recall = len(matches)/k\n",
    "            results = results.append(\n",
    "                pd.DataFrame({\n",
    "                    'search_time': [lsh_time / flat_time],\n",
    "                    'recall': [recall],\n",
    "                    'nbits': [nbits],\n",
    "                    'nb': [nb]\n",
    "                }), ignore_index=True\n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='nb', ylabel='recall'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "sns.lineplot(data=results, x='nb', y='recall', hue='nbits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='nb', ylabel='search_time'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "sns.lineplot(data=results, x='nb', y='search_time', hue='nbits')\n",
    "sns.lineplot(x=[0, 1_000_000], y=[1, 1], style=True, dashes=[(2,2)], color='r')"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "We certainly need to be pushing our `nbits` values high to get reasonable recall values, but we also risk making our search slower than an *exhaustive* search - so there is a bit of balancing required to find the sweet spot for each use-case."
   ]
  }
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